Court congestion is a worldwide phenomenon with negative societal effects such as delayed access to justice and drawn-out procedures, as well as negative economic ramifications. In this paper, we report on a field study conducted in cooperation with the Jerusalem District Court in Israel with the aim of reducing average case processing time within the existing set of resources. To achieve this goal, we analyzed the judicial process using operational concepts that have been successfully implemented in other contexts, and we adapted them to the court process. We further collected data before and after the operational changes were implemented to assess the efficacy of the implementation. We exploited the distinctive structure of the judicial process and the fact that our operational changes targeted one part of that process to propose a variation of the difference-in-differences approach to measure the process improvement. The results suggest that the duration of the treated part of the process was reduced by 46.1% postimplementation. We further examined the effect of the treatment on the quality of the process, measured by appeal rate, and found no adverse effect. These analyses demonstrate the potential of using operational management tools and concepts to improve judicial processes and to contribute to the alleviation of court congestion.
Motivation:
Prior investigation of the Israeli court system demonstrates that the judges constitute the bottleneck in the process (Bar-Niv et al. 2010, Azaria et al. 2023). Given that budget constraints in the Israeli Justice Department preclude any increase in the number of judges, our focus was on improving the overall throughput and lead time within the existing resource envelope.
The methodology using Continuous Hearing Scheduling:
As noted earlier, before the implementation, most judges adopted an unplanned, ad hoc scheduling policy that could be described as a hearing-level FIFO system. Under this policy, a new session is not scheduled until the previous session for the case in question has been completed. Bray et al. (2016) show that this system can be optimal when new cases have a higher probability of coming to an end in the next session than old cases. In our setting, because the pretrial proceedings have exhausted the judge’s opportunity to achieve a settlement (particularly with the new and extended pretrial session described in the previous subsection), it follows that the probability of new evidence-phase cases coming to an end in the coming session should be lower than for older cases. This provides the theoretical justification for seeking an alternative scheduling policy.
The new policy implemented in this study is a case-level FIFO scheduling system. This means that the judge, making use of the time estimates set out in the final pretrial session, schedules all future sessions required for hearings and for closing arguments in advance, rather than scheduling each session upon the completion of the previous one. This policy also effectively sets a case load limit for trial phase cases, which follows the simple method proposed by Yamatani et al. (2009).
Result:
The operational changes implemented in the current study focused on the trial phase, so they were only expected to have a notable effect on the duration of the trial phase and not on the pretrial phase. This distinction allowed us to base our identification strategy on the rationale of the difference-in-differences (DID) approach to estimate the effect of the modifications on the case processing time, as the trial phase serves as the treatment group and the pretrial phase as a control group. Our analyses suggest a significant reduction of 9.04 months, which is equivalent to 46.1% of the expected duration of the trial phase in the absence of the modifications.
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